We have demonstrated that the image representations extracted from pre-trained deep residual networks can be effectively used for benthic marine image classification in general and kelps in particular. These powerful and generic features outperform traditional off-the-shelf CNN features, which have already shown superior performance over conventional hand-crafted features [1]}, [2]}. The sibling and inclusive hierarchical training methods further enhance performance when compared to flat multi-class classification methods. The sibling and inclusive training methods show comparatively similar performance. However, the sibling method is superior because it has lower training time than the inclusive method. Furthermore, estimations of kelp cover by automated DRF classification closely resemble those of manual expert classifications with the added advantage of faster processing times. This work provides evidence that automatic annotations may save resources and time while providing effective estimates of benthic cover.
